Predicting Stock and Portfolio Returns Using Mixtures of Truncated Exponentials

Authors:
Barry R. Cobb;Rafael Rumí;Antonio Salmerón
Affiliations:
Department of Economics and Business, Virginia Military Institute, Lexington, Virginia, USA;Department of Statistics and Applied Mathematics, University of Almería, Almería, Spain;Department of Statistics and Applied Mathematics, University of Almería, Almería, Spain
Venue:
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Year:
2009

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Abstract

This paper presents mixtures of truncated exponentials (MTE) potentials in two applications of Bayesian networks to finance problems. First, naive Bayes and TAN models where continuous probability densities are approximated by MTE potentials are used to provide a distribution of stock returns. Second, a Bayesian network is used to determine a return distribution for a portfolio of stocks. Using MTE potentials to approximate the distributions for the continuous variables in the network allows use of the Shenoy-Shafer architecture to obtain a solution for the marginal distributions. We also illustrate the problem that arises in these models where deterministic relationships between variables appear, which is related to the partitioning of the domain of the MTE distributions. We propose a solution based on simulation.